@@ -26,7 +26,7 @@ CodeGraph now writes Ollama/LM Studio embeddings directly into SurrealDB’s ded
2626``` bash
2727export CODEGRAPH_EMBEDDING_PROVIDER=ollama
2828export CODEGRAPH_EMBEDDING_MODEL=qwen3-embedding:0.6b # or all-mini-llm, qwen3-embedding:4b, embeddinggemma etc.
29- export CODEGRAPH_EMBEDDING_DIMENSION=1024 # 384, 768, 1024, 2048, or 4096
29+ export CODEGRAPH_EMBEDDING_DIMENSION=1024 # 384, 768, 1024, 1536, 2048, 2560, 3072 or 4096 dimensions supported
3030
3131# Optional local reranking (LM Studio exposes an OpenAI-compatible reranker endpoint)
3232export CODEGRAPH_RERANKING_PROVIDER=lmstudio
@@ -170,7 +170,7 @@ Pick the setup that matches your needs:
170170
171171** Providers:**
172172- ** Embeddings:** Jina (You get 10 million tokens for free when you just create an account!)
173- - ** LLM:** Anthropic Claude or OpenAI GPT-5-*
173+ - ** LLM:** Anthropic Claude or OpenAI GPT-5.1 -*
174174- ** Backend** : SurrealDB graph database (You get a free cloud instance up-to 1gb! Or run it completely locally!)
175175
176176** Pros:** ✅ Best quality, ✅ Fast, ✅ 1M context (sonnet[ 1m] )
@@ -384,6 +384,7 @@ dimension = 2048
384384enabled = true
385385provider = " openai"
386386model = " gpt-5-codex-mini"
387+ context_window =200000
387388openai_api_key = " sk-..."
388389max_completion_token = 128000
389390reasoning_effort = " medium" # reasoning models: "minimal", "medium", "high"
@@ -403,6 +404,8 @@ jina_reranking_model = "jina-reranker-v3"
403404enabled = true
404405provider = " anthropic"
405406model = " claude-haiku"
407+ context_window = 200000
408+ max_completion_tokens = 25000
406409anthropic_api_key = " sk-ant-..."
407410```
408411
@@ -412,7 +415,7 @@ anthropic_api_key = "sk-ant-..."
412415provider = " openai" # or "jina"
413416model = " text-embedding-3-small"
414417openai_api_key = " sk-..."
415- dimension = 2048
418+ dimension = 1536
416419
417420[llm ]
418421enabled = true
@@ -472,6 +475,7 @@ dimension = 384
472475enabled = true
473476provider = " anthropic" # Best quality for analysis
474477model = " sonnet[1m]"
478+ context_window = 1000000
475479anthropic_api_key = " sk-ant-..."
476480```
477481
@@ -522,7 +526,7 @@ model = "haiku"
522526anthropic_api_key = " sk-ant-..."
523527context_window = 200000
524528temperature = 0.1
525- max_completion_token = 4096
529+ max_completion_token = 25000
526530
527531[performance ]
528532num_threads = 0 # 0 = auto-detect
@@ -663,8 +667,8 @@ flowchart TD
663667
664668 D -->|< 50K tokens| E1[Small Tier<br/>TERSE prompts<br/>5 max steps<br/>2,048 tokens]
665669 D -->|50K-150K tokens| E2[Medium Tier<br/>BALANCED prompts<br/>10 max steps<br/>4,096 tokens]
666- D -->|150K-500K tokens| E3[Large Tier<br/>DETAILED prompts<br/>15 max steps<br/>8,192 tokens]
667- D -->|> 500K tokens| E4[Massive Tier<br/>EXPLORATORY prompts<br/>20 max steps<br/>16,384 tokens]
670+ D -->|150K-400K tokens| E3[Large Tier<br/>DETAILED prompts<br/>15 max steps<br/>8,192 tokens]
671+ D -->|> 400K tokens| E4[Massive Tier<br/>EXPLORATORY prompts<br/>20 max steps<br/>16,384 tokens]
668672
669673 E1 & E2 & E3 & E4 --> F[Load Tier-Specific<br/>System Prompt]
670674
@@ -712,10 +716,10 @@ flowchart TD
712716** Key Components:**
713717
7147181 . ** Tier Detection** : Automatically adapts prompt complexity based on LLM's context window
715- - Small (<50K): Fast, terse responses for limited context models f.ex. local
719+ - Small (<50K): Fast, terse responses for limited context models f.ex. local gemma3 etc.
716720 - Medium (50K-150K): Balanced analysis for Claude Haiku, gpt-5.1-codex-mini
717- - Large (150K-400K): Detailed exploration for Sonnet, Opus, gpt-5.1
718- - Massive (>400K): Comprehensive deep-dives for grok-4-fast, gemini-2.5 -pro, Sonnet[ 1m]
721+ - Large (150K-400K): Detailed exploration for Sonnet, Opus, gpt-5.1, qwen3:4b
722+ - Massive (>400K): Comprehensive deep-dives for grok-4-fast, gemini-3.0 -pro, Sonnet[ 1m]
719723
7207242 . ** Multi-Step Reasoning** : ReAct pattern with tier-specific limits
721725 - Each step can call internal graph analysis tools
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